CHDP: Cooperative Hybrid Diffusion Policies for Reinforcement Learning in Parameterized Action Space
Bingyi Liu, Jinbo He, Haiyong Shi, Enshu Wang, Weizhen Han, Jingxiang Hao, Peixi Wang, Zhuangzhuang Zhang
Abstract
Hybrid action space, which combines discrete choices and continuous parameters, is prevalent in domains such as robot control and game AI. However, efficiently modeling and optimizing hybrid discrete-continuous action space remains a fundamental challenge, mainly due to limited policy expressiveness and poor scalability in high-dimensional settings. To address this challenge, we view the hybrid action space problem as a fully cooperative game and propose a Cooperative Hybrid Diffusion Policies (CHDP) framework to solve it. CHDP employs two cooperative agents that leverage a discrete and a continuous diffusion policy, respectively. The continuous policy is conditioned on the discrete action
BibTeX
@inproceedings{aaai2026_chdpcooperativeh,
title = {CHDP: Cooperative Hybrid Diffusion Policies for Reinforcement Learning in Parameterized Action Space},
author = {Bingyi Liu and Jinbo He and Haiyong Shi and Enshu Wang and Weizhen Han and Jingxiang Hao and Peixi Wang and Zhuangzhuang Zhang},
booktitle = {AAAI 2026},
year = {2026}
}